Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-free Cardiac Cine MRI using Deep Generative Learning

Motivation: Although late Gadolinium Enhancement (LGE) imaging is widely used for diagnosing myocardial infarction (MI), contrast-free approaches are in need for patients with gadolinium contraindications. Goal(s): To develop Cine Generated Enhancement (CGE), a novel technique that uses contrast-free cine images to predict images resembling LGE. Approach: A deep generative model was trained to translate cine images into LGE images of acute MI exploiting the different motion dynamics between heathy and infarcted myocardium. Results: Realistic enhancement images can be generated for acute MI patients using cine images unseen during training. The scar size and transmurality estimated with CGE agreed well with LGE. Impact: This study presents an effective, non-invasive, and contrast-free method for predicting LGE in acute MI, potentially reducing the use of gadolinium-based contrast agents and shortening cardiac MR examinations.